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Texture based Palm Print Recognition using Discrete Wavelet Transformation

by Dhulipalla Nagajyothi, K. Venkata Ramaiah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 3
Year of Publication: 2022
Authors: Dhulipalla Nagajyothi, K. Venkata Ramaiah
10.5120/ijca2022921991

Dhulipalla Nagajyothi, K. Venkata Ramaiah . Texture based Palm Print Recognition using Discrete Wavelet Transformation. International Journal of Computer Applications. 184, 3 ( Mar 2022), 40-43. DOI=10.5120/ijca2022921991

@article{ 10.5120/ijca2022921991,
author = { Dhulipalla Nagajyothi, K. Venkata Ramaiah },
title = { Texture based Palm Print Recognition using Discrete Wavelet Transformation },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2022 },
volume = { 184 },
number = { 3 },
month = { Mar },
year = { 2022 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number3/32316-2022921991/ },
doi = { 10.5120/ijca2022921991 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:34.474432+05:30
%A Dhulipalla Nagajyothi
%A K. Venkata Ramaiah
%T Texture based Palm Print Recognition using Discrete Wavelet Transformation
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 3
%P 40-43
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biometric based recognition is a method of verification that relies on the biological characteristics of each individual. They are processed based on their duplicate properties of being identical, portable, and difficult to reproduce. This paper represents a model for palm-print recognition system based on texture features using wavelet transformation. This is epitome for keeping personnel entry records in large enterprises. Using the simple approaches Discrete Wavelet Transformation (DWT) and Standard Deviation (σ), extraction of some texture characteristics from the ROI images of size 128X128. The images are then matched using Canberra Distance, Euclidean Distance and Manhattan distance. Among K images per user are used to create the training set, with K ranging from 1 to 4 and remaining images per user used for testing. In image recognition mode, the results are compared to the remaining images. The true acceptance rate is 98.86% obtained by using of Canberra distance.

References
  1. Aravind Nalamothu, et.al “Texture based Palmprint Recognition using Simple Methods”.International Journal of ComputerApplications, July 2012. 10.5120/7761-0827
  2. W. Li, et.al “Palmprint Identification by Fourier Transform”, International Journal of Pattern Recognition and Artificial Intelligence, 417-432-2002
  3. J. Lu, et.al “Enhanced Gabor-based region covariance matrices for Palmprint Recognition”, International journal of Electronics Letters, Aug 2009.
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  8. Hafiz Imtiaz, et. al “A Spectral Domain Dominant Feature Extraction Algorithm for Palmprint Recognition”, International Journal of Image processing(IJIP), Volume-5,2011
  9. A. Kong, et. al “ A survey of palmprint recognition”, pattern recognition, vol.42,2009
  10. “Digital Image Processing”, 3rd Edition, by Rafael C. Gonzalez, Richard E. Woods
  11. PolyU Palmprint Database, http://www.comp.polyu.edu.hk/~biometrics/.
Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Palmprint Recognition Texture Based Recognition Canberra Distance Euclidian Distance Manhattan distance Wavelet Transformation.

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